SoroushVahidi/scidocs huggingface dataset:
Youth texting: Help or hindrance to literacy?
(2017)

An extensive amount of research has been performed in recent years into the widespread practice of text messaging in youth. As part of this broad area of research, the associations between youth texting and literacy have been investigated in a variety of contexts. A comprehensive, semi-systematic review of the literature into texting and literacy was conducted, with a particular focus on quantitative empirical studies. Media reports, teacher surveys, and qualitative studies were also taken into consideration as part of this wide-ranging examination of previous research into this phenomenon. There were no clear positive or negative links discovered between adolescent texting practices and literacy, with the research findings in this study area best summarized as mixed and inconclusive. More studies in this research area are required, especially of an experimental or longitudinal kind. In addressing the debate on the impact of texting on literacy, research analyses as well as media reports would benefit from a more balanced presentation of existing findings.
Deep Neural Networks for i-Vector Language Identification of Short Utterances in Cars
(2016)

This paper is focused on the application of the Language Identification (LID) technology for intelligent vehicles. We cope with short sentences or words spoken in moving cars in four languages: English, Spanish, German, and Finnish. As the response time of the LID system is crucial for user acceptance in this particular task, speech signals of different durations with total average of 3.8s are analyzed. In this paper, the authors propose the use of Deep Neural Networks (DNN) to model effectively the i-vector space of languages. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNNs. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/LDA systems considering the effect of durations of signals. It is shown that the signals with durations between 2 and 3s meet the requirements of this application, i.e., high accuracy and fast decision, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.
Cloud RAN challenges and solutions
(2017)

In this paper we take an overall look at key technical challenges in the evolution of the Radio Access Network (RAN) architecture towards Cloud RAN, and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of General-Purpose Processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. Keywords—Cloud RAN, fronthaul, real-time, pooling, elastic scaling, multi-cell coordination, orchestration, NFV, 5G.
Cyberbullying classification using text mining
(2017)

Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes method and Support Vector Machine (SVM) then applying n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. Naive Bayes yields an average accuracy of 92.81%, SVM with a poly kernel yields an average accuracy of 97.11% It can be concluded that SVM with poly kernel yields higher accuracy than SVM with other kernels, Naive Bayes, and Kelly Reynolds research method of decision tree (J48) and k-NN.
Compliance by design for artifact-centric business processes
(2013)

Compliance to legal regulations, internal policies, or best practices is becoming a more and more important aspect in business processes management. Compliance requirements are usually formulated in a set of rules that can be checked during or after the execution of the business process, called compliance by detection. If noncompliant behavior is detected, the business process needs to be redesigned. Alternatively, the rules can be already taken into account while modeling the business process to result in a business process that is compliant by design. This technique has the advantage that a subsequent verification of compliance is not required. This paper focuses on compliance by design and employs an artifactcentric approach. In this school of thought, business processes are not described as a sequence of tasks to be performed (i. e., imperatively), but from the point of view of the artifacts that are manipulated during the process (i. e., declaratively). We extend the artifact-centric approach to model compliance rules and show how compliant business processes can be synthesized automatically.
Low Cost Dual Polarized Base Station Element for Long Term Evolution
(2014)

A broadband low profile dual polarized patch antenna for fourth generation long term evolution (4G LTE) is presented in this communication. By employing two wideband feeding mechanisms for the two input ports, a dual polarized patch antenna element with the characteristics of high input port isolation and wide impedance bandwidth is successfully designed. A wide meandering probe (M-probe) and a pair of twin-L-probes are proposed to feed a patch for dual polarization. The proposed design with a wideband balun has impedance bandwidths of over 47% at the two input ports as well as over 37 dB isolation within the entire operating band. Over 8 dBi gain can be achieved at the two ports.
Mitigating DoS attacks against signature-based authentication in VANETs
(2012)

Vehicular ad hoc networks (VANETs) are supposed to improve traffic safety and drivers' experiences. In a typical VANET, a vehicle broadcasts safety messages to its neighbors. Since behaviors based on safety messages could be life-critical, authentication of these messages must be guaranteed. Many signature-based schemes have been proposed for authentication in VANETs, but few of them have addressed the problem of denial of service (DoS) attacks against signature-based authentication. In such a DoS attack, attackers can broadcast forged messages with invalid signatures to force the receiving vehicles to perform lots of unnecessary signature verifications, and thus the benign vehicles cannot verify the messages from other legitimate vehicles. Our scheme features a pre-authentication process before signature verifying process to deal with this kind of DoS attack. The pre-authentication process takes advantage of the one-way hash chain and a group rekeying scheme. Evaluations show that the proposed scheme mitigates such DoS attacks effectively.
Pareto Optimal Security Resource Allocation for Internet of Things
(2017)

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution.
Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images
(2018)

The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.
Control Knowledge to Improve Plan Quality
(1994)

Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledge has been aimed at improving the efficiency of planning; this work has been termed “speed-up learning”. This paper focuses on learning control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.
Multi-Scale Convolutional Neural Networks for Time Series Classification
(2016)

Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls short of classification accuracy and efficiency. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, based on which an off-the-shelf classifier can be applied. These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance. Plus, most existing methods fail to take into account the fact that time series often have features at different time scales. To address these problems, we propose a novel end-to-end neural network model, Multi-scale Convolutional Neural Network (MCNN), which incorporates feature extraction and classification in a single framework. Leveraging a novel multi-branch layer and learnable convolutional layers, MCNN automatically extracts features at different scales and frequencies, leading to superior feature representation. MCNN is also computationally efficient, as it naturally leverages GPU computing. We conduct comprehensive empirical evaluation with various existing methods on a large number of benchmark datasets, and show that MCNN advances the state-of-the-art by achieving superior accuracy performance than other leading methods.
Mobile commerce adoption in China and the United States: a cross-cultural study
(2009)

Mobile communication technologies have penetrated consumer markets throughout the world. Mobile commerce is likely to make a strong influence on business activities, consumer behavior, as well as national and global markets. Thus the identification of factors that influence mobile commerce adoption has significant value. In a global context, this study identified nine factors affecting mobile commerce adoption based on published research in MIS. These factors were investigated in China and the United States, and a comparative examination was conducted. A survey was conducted on 190 individual mobile commerce users in China and USA. Results show that there are several significant differences among the antecedents and their impacts on consumer intention to use mobile commerce in the two cultural settings. The study provides a number of practical insights and informs vendors seeking to enter the Chinese and the US marketplace with specific information on user perspectives.
The effects of perceived risk and technology type on users' acceptance of technologies
(2008)

Previous studies on technology adoption disagree regarding the relative magnitude of the effects of perceived usefulness and perceived ease of use. However these studies did not consider moderating variables. We investigated four potential moderating variables – perceived risk, technology type, user experience, and gender – in users’ technology adoption. Their moderating effects were tested in an empirical study of 161 subjects. Results showed that perceived risk, technology type, and gender were significant moderating variables. However the effects of user experience were marginal after the variance of errors was removed. # 2007 Published by Elsevier B.V. www.elsevier.com/locate/im Available online at www.sciencedirect.com Information & Management 45 (2008) 1–9
Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet
(2006)

This study examines the utility of three prospective models for understanding the continued IT usage behavior. The three models include: Expectation-Confirmation Model in IT Domain (ECM-IT), Technology Acceptance Model (TAM), and a hybrid model integrating TAM and ECM-IT (extended ECM-IT). Based on a survey of 1826 mobile Internet users, the LISREL analysis shows that all three models meet the various goodness-of-fit criteria. When compared using special indices for differentiating among alternative good models, TAM has the best fit to the data followed by ECM-IT, and the extended ECM-IT. In terms of variance explained for intention to continue IT usage, the extended ECM-IT has the highest R (67%) followed by TAM (63%), and ECM-IT (50%). We conclude that TAM is the most parsimonious and generic model that can be used to study both initial and continued IT adoption; the extended ECM-IT explains continued IT usage behavior as well as TAM; and both the ECM-IT and extended ECM-IT models provide additional information to increase our understanding of continued IT usage. © 2006 Elsevier B.V. All rights reserved.
Trust and TAM in Online Shopping: An Integrated Model
(2003)

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A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data
(2008)

Recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. Thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. This model is built as micro-clusters using semi-supervised clustering technique and classification is performed with kappa-nearest neighbor algorithm. An ensemble of these models is used to classify the unlabeled data. Empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training, outperforms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.
Image Retrieval by Examples
(2000)

A currently relevant research field in information sciences is the management of nontraditional distributed multimedia databases. Two related key issues are to achieve an efficient content-basedquery by exampleretrieval and a fast response time. This paper presents the architecture of a distributed image retrieval system which provides novel solutions to these key issues. In particular, a way to quantify the effectiveness of low level visual descriptors in database query tasks is presented. The results are also used to improve the system response time, which is an important issue when querying very large databases. A new mechanism to adapt system query strategies to user behavior is also introduced in order to improve the effectiveness of relevance feedback and overall system response time. Finally, the issue of browsing multiple distributed databases is considered and a solution is proposed using multidimensional scaling techniques.
Caring for a child with autism spectrum disorder and parents' quality of life: application of the CarerQol.
(2014)

This study describes the impact of caregiving on parents of children with autism spectrum disorders (ASDs). Secondly, we investigate construct validation of the care-related quality of life instrument (CarerQol) measuring impact of caregiving. Primary caregivers of children with ASDs were included. Many parents experienced considerable problems combining daily activities with care, had financial problems or suffered from depressive mood. Validity tests showed that a higher impact of caring on the CarerQol was positively associated with higher subjective burden and lower family quality of life. Most of the associations between CarerQol scores and background characteristics confirmed previous research. The CarerQol validly measures the impact of caregiving for children with ASDs on caregivers in our sample. The CarerQol may therefore be useful for including parent outcomes in research on ASDs.
Image recognition of plant diseases based on principal component analysis and neural networks
(2012)

Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.
Damage in the anca-associated vasculitides: long-term data from the European vasculitis study group (EUVAS) therapeutic trials.
(2015)

OBJECTIVES
To describe short-term (up to 12 months) and long-term (up to 7 years) damage in patients with newly diagnosed antineutrophil-cytoplasm antibody-associated vasculitis (AAV).


METHODS
Data were combined from six European Vasculitis Study group trials (n=735). Long-term follow-up (LTFU) data available for patients from four trials (n=535). Damage accrued was quantified by the Vasculitis Damage Index (VDI). Sixteen damage items were defined a priori as being potentially treatment-related.


RESULTS
VDI data were available for 629 of 735 patients (85.6%) at baseline, at which time 217/629 (34.5%) had ≥1 item of damage and 32 (5.1%) ≥5 items, reflecting disease manifestations prior to diagnosis and trial enrolment. LTFU data were available for 467/535 (87.3%) at a mean of 7.3 years postdiagnosis. 302/535 patients (56.4%) had VDI data at LTFU, with 104/302 (34.4%) having ≥5 items and only 24 (7.9%) no items of damage. At 6 months and LTFU, the most frequent items were proteinuria, impaired glomerular filtration rate, hypertension, nasal crusting, hearing loss and peripheral neuropathy. The frequency of damage, including potentially treatment-related damage, rose over time (p<0.01). At LTFU, the most commonly reported items of treatment-related damage were hypertension (41.5%; 95% CI 35.6 to 47.4%), osteoporosis (14.1%; 9.9 to 18.2%), malignancy (12.6%; 8.6 to 16.6%), and diabetes (10.4%; 6.7 to 14.0%).


CONCLUSIONS
In AAV, renal, otolaryngological and treatment-related (cardiovascular, disease, diabetes, osteoporosis and malignancy) damage increases over time, with around one-third of patients having ≥5 items of damage at a mean of 7 years postdiagnosis.